• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.1
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• pp.70-75
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• 2018

Smart Factory is an intelligent factory that can enhance productivity, quality, customer satisfaction, etc. by applying information and communications technology to the entire production process including design & development, manufacture, and distribution & logistics. The precise amount of data generated in a smart factory varies depending on the factory's size and state of facilities. Regardless, it would be difficult to apply traditional production management systems to a smart factory environment, as it generates vast amounts of data. For this reason, the need for a distributed big-data processing system has risen, which can process a large amount of data. Therefore, this article has designed a Gluster File System (GlusterFS)-based distributed big-data processing system that can be used in a smart factory environment. Compared to existing distributed processing systems, the proposed distributed big-data processing system reduces the system load and the risk of data loss through the distribution and management of network traffic.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.65-66
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• 2021

본 논문에서는 Smart Factory의 자동 공정에서 결함의 분류를 실시간으로 시도하여 자동 공정 제어를 위한 결함 분류 딥러닝 기법을 제안하고, Pooling 종류에 따른 분류 성능을 비교한다. Smart Factory 구축에 있어서 CNN을 이용한 공정 제어를 통해 제품 생산에 있어서 생산량의 증가와 불량률의 감소를 이루어내는 것이 가능하다. Smart Factory는 자동화 공정이므로 결함의 분류 속도가 중요하지만, 생산량의 증가와 불량률의 감소를 위해서는 정확하게 결함의 종류를 분류하여 Smart Factory의 공정을 제어하는 것이 더욱 중요하다. 본 논문에서는 Pooling을 Max Pooling과 Averrage Pooling을 복합적으로 설정하였을 때 높은 성능을 보였다.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.109-121
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• 2020

As a decrease in labor became a serious issue in the manufacturing industry, smart factory technology, which combines IT and the manufacturing business, began to attract attention as a solution. In this study, we have designed and implemented a real-time remote management system for smart factories, which is connected to an IoT sensor and gateway, for plastic manufacturing plants. By implementing the REST API in which an IoT sensor and smart gateway can communicate, the system enabled the data measured from the IoT sensor and equipment status data to the real-time monitoring system through the gateway. Also, a web-based management dashboard enabled remote monitoring and control of the equipment and raw material processing status. A comparative analysis experiment was conducted on the suggested system for the difference in processing speed based on equipment and measurement data number change. The experiment confirmed that saving equipment measurement data using cache mechanisim offered faster processing speed. Through the result our works can provide the basic framework to factory which need implement remote management system.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1071-1082
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• 2021

Since the smart factory has been recently recognized as an industrial core requirement, various mechanisms to ensure efficient and stable operation have attracted much attention. This attention is based on the fact that in a smart factory environment where operating processes, such as facility control, data collection, and decision making are automated, the disruption of processes due to problems such as facility anomalies causes considerable losses. Although many studies have considered methods to prevent such losses, few have investigated how to effectively apply the solutions. This study proposes a Kubernetes based system applied in a smart factory providing effective operation and facility management. To develop the system, we employed a useful and popular open source project, and adopted deep learning based anomaly detection model for multi-sensor anomaly detection. This can be easily modified without interruption by changing the container image for inference. Through experiments, we have verified that the proposed method can provide system stability through nondisruptive maintenance, monitoring and non-disruptive updates for anomaly detection models.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.97-117
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• 2020

Five years have passed since the introduction of the smart factory amid the new opportunities for growth and job creation in relation to domestic manufacturing companies. Nevertheless, there is a lack of analysis on SMEs introduction smart factories. This study empirically analyzed the effects on the introduction of smart factories of domestic metal processing SMEs by distinguishing the characteristics of enterprises In this study, 103 companies which introduced smart factories and another 106 companies which did not introduce them were sampled. The Introduction of the Smart Factory was analyzed by four categories such as the Company characteristics (R&D capability, product production capability, organizational change), entrepreneur characteristics (risk sensitivity), relational characteristics (trust, dependence, cooperation, Influence), and structural characteristics (competition). As a result of the research, we found out product production capacity, risk sensitivity, trust and cooperation, Influence, and competition are statistically significant in the introduction of smart factory. But competition was characterized by a negative (-) sign opposite to the hypothesis. This study is meaningful in that the scope of the analysis has been expanded by analyzing whether smart factory was introduced or not considering the characteristics of the company. And there should be continuous research on its utilization as well as the introduction of smart factory.

• Journal of Convergence for Information Technology
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• v.8 no.2
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• pp.233-238
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• 2018

SMEs and small enterprises are making various attempts to manage SMEs in terms of equipment, safety and energy management as well as production management. However, SMEs do not have the investment capacity and it is not easy to build a smart factory to improve management and productivity of SMEs. In this paper, we propose a smart factory construction algorithm that partially integrates the factory equipment currently operated by SMEs. The proposed algorithm supports collection, storage, management and processing of product information and release information through IoT device during the whole manufacturing process so that SMEs' smart factory environment can be constructed and operated in stages. In addition, the proposed algorithm is characterized in that central server manages authentication information between devices to automate the linkage between IoT devices regardless of the number of IoT devices. As a result of the performance evaluation, the proposed algorithm obtained 13.7% improvement in the factory process and efficiency before building the Smart Factory environment, and 19.8% improvement in the processing time in the factory. Also, the cost of input of manpower into process process was reduced by 37.1%.

• Journal of the Korean Solar Energy Society
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• v.34 no.5
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• pp.43-52
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• 2014

A plant factory system is getting the spotlight as alternatives to cope with the weather anomaly and food crisis because of the global warming. A study on 'Plant Processing Factory System' has been proceeded to develope 'low-carbon green growth' since our government selected it as the green technologies in 2010. The plant factory has played a major role in growth industries connected to many other fields like low-carbon as well as lighting and automated system. This study is aimed to solve the problems on low productivity and health problem of plant workers caused by highly concentrated carbon dioxide and low temperature in each process in the plant factory. It is aimed to research data to understand the actual conditions of plant workers and improve the thermal environment.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.817-819
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• 2019

다양한 학습 모델이 발전하고 있는 지금, 학습을 통한 다양한 시도가 진행되고 있다. 이중 에너지 분야에서 많은 연구가 진행 중에 있으며, 대표적으로 BEMS(Building energy Management System)를 볼 수 있다. BEMS의 경우 건물을 기준으로 건물에서 생성되는 다양한 DATA를 이용하여, 에너지 예측 및 제어하는 다양한 기술이 발전해가고 있다. 하지만 FEMS(Factory Energy Management System)에 관련된 연구는 많이 발전하지 못했으며, 이는 BEMS와 FEAMS의 차이에서 비롯된다. 본 연구에서는 실제 공장에서 수집한 DATA를 기반으로 하여, 전력량 예측을 하였으며 예측을 위한 기술로 시계열 DATA 분석 방법인 LSTM 알고리즘을 이용하여 진행하였다.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.4 no.1
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• pp.33-42
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• 1994

This study was carried out to assess worker exposure to vinyl chloride monomer (VCM) and to present control measures in the factories processing and producing polyvinyl chloride (PVC) resin. The conclusion remarks are as follows. Only two personal samples in the factory ("E") processing polyvinyl chloride resin were analysed to be 27.6 ppm and 12.6 ppm, respectively. But, these concentration exceed 1 ppm, Permissible Exposure Limits (PEL) of OSHA. So, worker's exposure to VCM at "E" factory should be reevaluated. In "A", "B" and "C" factory producing polyvinyl chloride resin, the average worker's exposures to VCM were 0.12 ppm, 0.86 ppm and 1.23 ppm, respectivery. Worker exposure to VCM at distillation and dry process was higer than other processes at "A" factory. The average exposure concentration of worker at polymerization process of "B" and "C" factory was 1.23 ppm, and 1.46 ppm respcetively. These concentration exceed 1 ppm, Permissible Exposure Limits of OSHA. Control room of "B" and "C" factory had 0.91 ppm and 0.65 ppm of worker's exposure concentration respectively. "A" factory was evaluated to be "acceptable", but "B" and "C" factories were evaluated to be "not acceptable", by the workplace exposure assessment program of AIHA. Process other than bagging and control room of "A" factory was evaluated to "not acceptable". Immediate correction measures for preventing workers from exposure to VCM should be performed in the factories or process that were evaluated to be "not acceptable". After these control measures are taken, worker exposure to VCM must be reevaluated through personal air monitoring. Control measures presented by this study are complete sealing of connecting pipe lines, flanging, packing, bolting and nutting. Periodic leak test for leak parts is also required. And positive pressure facility should be constructed at control room of "B" and "C" factory. Fresh air through cleaner such as HEPA filter should be supplied to control room. In addition to these control measures, periodic personal monitoring for evaluating worker exposure to VCM should be performed.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.36.2-36
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• 2001

At present, information processing by computer is greatly concerned in our society. And robots controlled by computer are much introduced in a factory´s production line and so on, robot abilities develop robot obtain good results. And recently, robots greatly take part in not only limited place, for example a factory and so on, but also general a household. Some robots pleased people, others help humans task. Robots are sure to be great useful in nursing that as regarded our society as questionable. In this situation, we request that robots can take vision like human´s eyes ...